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Aircraft aerodynamic parameter detection using micro hot-film flow sensor array and BP neural network identification.

Que R, Zhu R - Sensors (Basel) (2012)

Bottom Line: Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft.For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed.A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China. katykob@163.com

ABSTRACT
Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft. For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed. A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters. Two different sensor arrangements are tested in wind tunnel experiments and dependence of the system performance on the sensor arrangement is analyzed.

No MeSH data available.


Related in: MedlinePlus

Comparative results of aerodynamic parameters.
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f9-sensors-12-10920: Comparative results of aerodynamic parameters.

Mentions: The performances of the trained neural networks were evaluated by comparing the actual flight parameters with the model-based flight parameters calculated from the sensors. The comparative results are shown in Figure 9, where the blue solid lines represent the calculated flight parameters by using the network model with the sensor measurements as its inputs, and the green dashed lines represent the actual flight parameters correspondingly. The detailed test processes shown in Figure 9 are: firstly, the airspeed was set from 0 to 28 m/s (4∼5 m/s per step) while the angle of attack and the angle of sideslip were zero; afterwards, the angle of sideslip was set from 0° to 20° (2° per step) at different flow airspeed varied from 5 to 28 m/s (5 m/s per step) while the angle of attack kept at zero; finally, the angle of attack was set from 0° to 20° (2° per step) at different airspeed varied from 5 to 28 m/s (5 m/s per step) while the angle of sideslip kept at zero.


Aircraft aerodynamic parameter detection using micro hot-film flow sensor array and BP neural network identification.

Que R, Zhu R - Sensors (Basel) (2012)

Comparative results of aerodynamic parameters.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472866&req=5

f9-sensors-12-10920: Comparative results of aerodynamic parameters.
Mentions: The performances of the trained neural networks were evaluated by comparing the actual flight parameters with the model-based flight parameters calculated from the sensors. The comparative results are shown in Figure 9, where the blue solid lines represent the calculated flight parameters by using the network model with the sensor measurements as its inputs, and the green dashed lines represent the actual flight parameters correspondingly. The detailed test processes shown in Figure 9 are: firstly, the airspeed was set from 0 to 28 m/s (4∼5 m/s per step) while the angle of attack and the angle of sideslip were zero; afterwards, the angle of sideslip was set from 0° to 20° (2° per step) at different flow airspeed varied from 5 to 28 m/s (5 m/s per step) while the angle of attack kept at zero; finally, the angle of attack was set from 0° to 20° (2° per step) at different airspeed varied from 5 to 28 m/s (5 m/s per step) while the angle of sideslip kept at zero.

Bottom Line: Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft.For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed.A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China. katykob@163.com

ABSTRACT
Air speed, angle of sideslip and angle of attack are fundamental aerodynamic parameters for controlling most aircraft. For small aircraft for which conventional detecting devices are too bulky and heavy to be utilized, a novel and practical methodology by which the aerodynamic parameters are inferred using a micro hot-film flow sensor array mounted on the surface of the wing is proposed. A back-propagation neural network is used to model the coupling relationship between readings of the sensor array and aerodynamic parameters. Two different sensor arrangements are tested in wind tunnel experiments and dependence of the system performance on the sensor arrangement is analyzed.

No MeSH data available.


Related in: MedlinePlus